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Title: Self-organizing neural network as a fuzzy classifier

Abstract

This paper describes a self-organizing artificial neural network, based on Kohonen`s model of self-organization, which is capable of handling fuzzy input and of providing fuzzy classification. Unlike conventional neural net models, this algorithm incorporates fuzzy set-theoretic concepts at various stages. The input vector consists of membership values for linguistic properties along with some contextual class membership information which is used during self-organization to permit efficient modeling of fuzzy (ambiguous) patterns. A new definition of gain factor for weight updating is proposed. An index of disorder involving mean square distance between the input and weight vectors is used to determine a measure of the ordering of the output space. This controls the number of sweeps required in the process. Incorporation of the concept of fuzzy partitioning allows natural self-organization of the input data, especially when they have ill-defined boundaries. The output of unknown test patterns is generated in terms of class membership values. Incorporation of fuzziness in input and output is seen to provide better performance as compared to the original Kohonen model and the hard version. The effectiveness of this algorithm is demonstrated on the speech recognition problem for various network array sizes, training sets and gain factors. 24 refs.

Authors:
;  [1]
  1. Indian Statistical Inst., Calcutta (India)
Publication Date:
OSTI Identifier:
57409
Resource Type:
Journal Article
Journal Name:
IEEE Transactions on Systems, Man, and Cybernetics
Additional Journal Information:
Journal Volume: 24; Journal Issue: 3; Other Information: PBD: Mar 1994
Country of Publication:
United States
Language:
English
Subject:
99 MATHEMATICS, COMPUTERS, INFORMATION SCIENCE, MANAGEMENT, LAW, MISCELLANEOUS; PATTERN RECOGNITION; ARTIFICIAL INTELLIGENCE; SPEECH; FUZZY LOGIC; ALGORITHMS; NEURAL NETWORKS

Citation Formats

Mitra, S, and Pal, S K. Self-organizing neural network as a fuzzy classifier. United States: N. p., 1994. Web. doi:10.1109/21.278989.
Mitra, S, & Pal, S K. Self-organizing neural network as a fuzzy classifier. United States. https://doi.org/10.1109/21.278989
Mitra, S, and Pal, S K. 1994. "Self-organizing neural network as a fuzzy classifier". United States. https://doi.org/10.1109/21.278989.
@article{osti_57409,
title = {Self-organizing neural network as a fuzzy classifier},
author = {Mitra, S and Pal, S K},
abstractNote = {This paper describes a self-organizing artificial neural network, based on Kohonen`s model of self-organization, which is capable of handling fuzzy input and of providing fuzzy classification. Unlike conventional neural net models, this algorithm incorporates fuzzy set-theoretic concepts at various stages. The input vector consists of membership values for linguistic properties along with some contextual class membership information which is used during self-organization to permit efficient modeling of fuzzy (ambiguous) patterns. A new definition of gain factor for weight updating is proposed. An index of disorder involving mean square distance between the input and weight vectors is used to determine a measure of the ordering of the output space. This controls the number of sweeps required in the process. Incorporation of the concept of fuzzy partitioning allows natural self-organization of the input data, especially when they have ill-defined boundaries. The output of unknown test patterns is generated in terms of class membership values. Incorporation of fuzziness in input and output is seen to provide better performance as compared to the original Kohonen model and the hard version. The effectiveness of this algorithm is demonstrated on the speech recognition problem for various network array sizes, training sets and gain factors. 24 refs.},
doi = {10.1109/21.278989},
url = {https://www.osti.gov/biblio/57409}, journal = {IEEE Transactions on Systems, Man, and Cybernetics},
number = 3,
volume = 24,
place = {United States},
year = {Tue Mar 01 00:00:00 EST 1994},
month = {Tue Mar 01 00:00:00 EST 1994}
}